There are racial/ethnic disparities in the diagnosis of autism spectrum disorder, including delayed diagnosis, discrimination, and a lack of culturally responsive care. The perspectives of caregivers of color are critical in improving delivery of equitable care. We systematically reviewed articles pertaining to experiences with the diagnostic process among caregivers of color. We entered key terms into five databases to identify literature from 2000 to 2021. Fifteen qualitative studies met inclusion criteria, representing 253 caregivers. We used inductive methods to examine themes across racial and ethnic groups and assessed the quality of included studies. Families of color identified multiple factors that negatively affected the diagnostic process. Systems-level factors included long wait lists and financial concerns. Provider-level factors included minimization of caregiver concerns, a “wait and see” approach, biases, and lack of knowledge. Caregivers also described individual (e.g. knowledge) and family factors (e.g. stigma) that delayed diagnosis and complicated the diagnostic process. Communication barriers were commonly reported, which impeded understanding of autism spectrum disorder. Some families described providers, other individuals, community networks, and self-advocacy as facilitators. Interventions targeting systems- (e.g. Medicaid expansion) and provider-level (e.g. increase training in autism spectrum disorder) factors are needed to increase equity in the autism spectrum disorder diagnostic process. Lay abstract Children of color are diagnosed with autism later than White children. Caregivers of color are also more likely than White caregivers to report that their child’s healthcare providers do not treat them as a partner, spend enough time with them, or respect their culture and values. We wanted to better understand the experiences of caregivers of color with the diagnostic process of autism spectrum disorder, from the time they discuss developmental concerns with their child’s primary care provider to when the diagnosis is shared with them. We systematically reviewed the literature and found 15 articles that explored the experiences of caregivers of color. Caregivers of color described that they faced large-scale barriers, such as the cost of appointments, transportation, and long wait lists. They also reported negative experiences with providers, including providers not taking their concerns seriously, making assumptions about caregivers, and delaying referrals for an evaluation. Caregivers stated that their own lack of knowledge of autism spectrum disorder, stigma, their family’s thoughts and opinions, and cultural differences between providers and caregivers served as barriers during the diagnostic process. Communication challenges were discussed and included use of medical and technical jargon, a lack of follow-up, language barriers, and difficulty obtaining high-quality interpreters. Some families described providers, other individuals, community networks, and self-advocacy as helpful during the diagnostic process. Large-scale changes are needed, such as increases in the number of providers who are trained in diagnosing Autism. Provider-level changes (e.g. implicit bias training) are also important for improving caregivers’ experiences.
Recommendations for easing the transition from pediatric to adult care for late adolescents with ADHD include heavily leveraging the doctor-patient relationship, and capturing the young adult's attention through technologies that already absorb them.
Objective: Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs. Method: This study utilized trial-level data from the stop signal task from 8916 children (9–10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences. Results: There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls’ wide boundary separation. Conclusions: Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.
Background Children with attention‐deficit/hyperactivity disorder (ADHD) have more sleep problems than their peers which contribute to behavioral and functional impairments. This study examines the bidirectional relationship between nightly sleep (i.e., total sleep time and sleep efficiency) and daily behavior of children with ADHD. Method Forty‐three children (ages 6–13 [mean = 9.05, 54% male, 77% medicated]) participated in a 2‐week study during an ADHD Summer Treatment Program (STP). Sleep was measured with actigraphy. Behavior was assessed using STP clinical data and daily parent and counselor ratings of ADHD symptoms, oppositional defiant disorder behaviors, and emotion regulation (e.g., difficulty regulating emotional disposition and controlling emotions). We hypothesized that healthier night's sleep measured by actigraphy (i.e., sleep efficiency and total sleep time [TST]) would relate to less ADHD symptoms, less emotional dysregulation, and better academic performance the next day. Additionally, we hypothesized that less ADHD symptoms, less emotional dysregulation, and greater academic performance would relate to healthier sleep that night. Results Higher nightly sleep efficiency was related to improved parent‐ratings of ADHD the next day (R2 = 0.04, p = 0.04) and improved parent‐ratings of ADHD during the day lead to higher sleep efficiency that night (R2 = 0.002, p = 0.02). Higher rates of daily assignment completion were related to higher sleep efficiency at night (R2 = 0.035, p = 0.03). TST was not related to any behavioral outcomes. Conclusion Sleep efficiency may be more relevant than TST to behavioral performance the next day. Additionally, a bidirectional relationship exists between sleep efficiency and parent ratings of ADHD. Findings highlight the importance of assessing for manifestations of poor sleep efficiency, waking minutes, and wakings after sleep onset when diagnosing and treating ADHD.
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